Parallel AI’s Task API is the closest neighbor to Entity Enricher: give it an entity and a JSON schema, and a web-research agent fills the fields, with citations and a confidence score per field. The difference is in how each platform earns trust in a value — and in what happens to the result afterwards. Entity Enricher cross-checks fields across several independent LLMs, reads your own documents, deduplicates linked entities with semantic IDs, and persists everything as relational tables in your own database.
2+ independent LLMs answer the same schema; field-level disagreements are detected and resolved by rule or an AI arbiter, with the reasoning recorded.
One research pipeline per task. Confidence is real and per-field, but it is the same system grading its own answer.
LLM world knowledge, live web search, and your own documents (PDFs, Office files, images, audio) as first-class sources.
Live web research with citations — strong for fresh public facts, but your private documents are not a source.
Enrichments persist as entity state and mirror into your own PostgreSQL as real relational tables — SQL snapshot plus an idempotent delta feed — while semantic IDs collapse duplicate linked entities into one keyed row.
Each run returns a JSON object. Storage, identity, deduplication, and reconciliation with earlier runs are yours to build.
Bring your own LLM API keys and pay your providers directly for tokens — with prompt caching and per-call cost visibility. Self-hosted models supported.
Fixed per-request pricing by processor depth (published at $5–$2,400 per 1,000 runs). Simple to budget; no BYOK.
| Feature | Entity Enricher | Parallel AI |
|---|---|---|
| Custom output schema (any nesting) | ||
| Any entity type you define | ||
| Sources | LLM knowledge + your docs + web | Live web research |
| Multi-model fan-out (2+ LLMs in parallel) | ||
| Field-level fusion & conflict resolution | ||
| Per-field evidence | Arbitration audit trail | Citations + confidence |
| Semantic IDs (linked-entity dedup) | ||
| Relational sync to your own database | ||
| Document ingestion (PDF, DOCX, images) | ||
| Pre-flight entity classification | ||
| Multilingual output (40 languages) | ||
| Bring your own keys / self-hosted models | ||
| Batch processing | Task groups | |
| REST API + MCP + n8n / Make surfaces | API + SDK | |
| Pricing Model | Pay-per-token (BYOK) | Per-request processor tiers |
Pay-per-token
Bring your own LLM API keys and pay your providers directly for tokens consumed. Prompt caching keeps multi-model runs cheap; every call reports its exact cost.
Per-request tiers
Fixed price per run by processor depth — published tiers span $5 to $2,400 per 1,000 runs, with enrichment-style tasks around $5-25 per 1,000 rows. No BYOK; the research stack and its model bill are Parallel’s.
Pricing reflects publicly published tiers and can change — check each vendor for current rates.
Exa Websets and Bright Data’s Deep Lookup solve a neighboring problem: turning a natural-language query into a list of matching entities with criteria columns. They discover entities; Entity Enricher and Parallel enrich entities you already have. If discovery is your bottleneck, pair one of them upstream of your enrichment step.
Run several independent models on your schema, arbitrate their conflicts with the reasoning recorded, and land the result in your own database — deduplicated and keyed.
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